On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,181 papers · 148 categories

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13 results for “Co-Optimization”

Co-optimization of Energy and Reserve with Incentives to Wind Generation: Case Studyq-fin.GN

Case study shows impact of co-optimizing energy and reserve for wind energy.

problem Impact of lack of co-optimization of energy and reserve in high wind penetration scenarios.

method Developed two models with and without co-optimization, calibrated with Spanish market parameters.

result Models show significant differences in energy and reserve management.

wind energy reserve management co-optimization Spanish market sensitivity analysis

2021-07-03abs ↗pdf ↗

CO-Optimal Transportstat.ML

A new CO-Optimal Transport method optimizes transport maps between samples and features.

problem Optimal transport's limitations when samples are on different spaces.

method COOT: a novel OT problem that optimizes transport maps between samples and features simultaneously.

result COOT leads to performance improvements over state-of-the-art methods in domain adaptation and co-clustering.

Optimal Transport Machine Learning Domain Adaptation Co-clustering Feature Matching

2020-02-10abs ↗pdf ↗

Battery Storage Co-Optimization in Day-Ahead and Real-Time Markets with Bayesian Optimizationq-fin.CP

ARBO-DART optimizes battery storage dispatch in day-ahead and real-time markets.

problem Optimizing battery storage dispatch in day-ahead and real-time markets.

method Adaptive Refinement Bayesian Optimization (ARBO) for Day-Ahead and Real-Time (ARBO-DART) markets.

result ARBO-DART optimizes battery storage dispatch without requiring analytic gradients or finite-scenario approximations.

Battery Storage Co-Optimization Bayesian Optimization Day-Ahead Markets Real-Time Markets

2026-08-02abs ↗pdf ↗

Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Frameworkcs.LG

This paper optimizes deep learning systems for high performance and low energy consumption.

problem Achieving ultra-high energy efficiency and performance for deep neural networks.

method Developed an algorithm-hardware co-optimization framework that reduces computational and storage complexity.

result Achieved at least 152X speedup and 71X energy efficiency gain compared to IBM TrueNorth processor.

deep learning energy efficiency performance optimization algorithm-hardware co-optimization FPGA

2018-02-18abs ↗pdf ↗

Unbalanced CO-Optimal Transportstat.ML

Unbalanced COOT improves feature alignment robustly to outliers.

problem Optimal transport methods are sensitive to outliers in real-world data.

method COOT infers alignment between features and samples, unbalanced COOT adds robustness.

result Unbalanced COOT is robust to noise in real-world datasets.

optimal transport feature alignment robustness outliers real-world data

2022-05-30abs ↗pdf ↗

Co-optimization for Adaptive Conformal Predictionstat.ML

CoCP optimizes prediction intervals by jointly learning center and radius, improving efficiency and coverage.

problem Inefficient conformal prediction intervals under heteroscedasticity and skewness.

method Co-optimization framework that learns center and radius through alternating optimization steps.

result CoCP yields consistently shorter intervals and state-of-the-art conditional coverage diagnostics.

conformal prediction heteroscedasticity skewness finite-sample validity center-radius optimization

2026-03-02abs ↗pdf ↗

Geometry of the Space of Partitioned Networks: A Unified Theoretical and Computational Frameworkmath.MG

Unified framework for higher-order network analysis.

problem Complex structure of space of networks.

method Measure-theoretic formalism, Gromov-Wasserstein distance, co-optimal transport distance.

result Unified theoretical treatment of generalized networks.

networks higher-order relations geometry Alexandrov space curvature

2024-09-10abs ↗pdf ↗

PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Designcs.LG

PABO optimizes DNN and hardware hyperparameters for efficient edge device acceleration.

problem Joint optimization of DNN accuracy and hardware cost for edge devices.

method Bayesian optimization with pseudo agent-based approach for memristive crossbar accelerators.

result PABO achieves significant speed-ups and superior performance compared to state-of-the-art methods.

Bayesian optimization memristive crossbar hyperparameter optimization edge devices DNN acceleration

2019-06-11abs ↗pdf ↗

Variable selection for Gaussian process regression through a sparse projectioncs.LG

New method selects variables for GP regression using sparse projection.

problem Identifying environmental factors affecting metal corrosion.

method Sparse projection of input variables, gradient descent optimization, non-convex marginal likelihood.

result Proposed method outperforms benchmarks in variable selection accuracy.

variable selection Gaussian process regression sparse projection covariance modeling gradient descent

2020-08-25abs ↗pdf ↗

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecastingmath.OC

New framework optimizes forecasting and decision-making in dynamic systems.

problem Optimizing forecasting and decision-making processes in dynamic systems.

method Closed-loop framework using bilevel optimization.

result The proposed methodology yields consistently better performance than the standard open-loop approach.

forecasting optimization dynamic systems bilevel optimization reserve requirements

2021-02-26abs ↗pdf ↗

Generative Meta-Learning Robust Quality-Diversity Portfoliocs.NE

Proposes a meta-learning method for robust portfolio optimization.

problem Optimizing a robust portfolio ensemble with diverse sub-portfolios.

method Uses a deep generative model with convolutional, LSTM, and dense layers to generate diverse sub-portfolios.

result The ensemble portfolio is robust and generalizes well, balancing performance and diversity.

meta-learning portfolio optimization robustness diversity deep generative model

2023-07-15abs ↗pdf ↗

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embeddingcs.LG

GraphVite efficiently trains node embeddings for large networks.

problem Scaling node embedding algorithms to handle large networks (tens of millions of nodes).

method CPU-GPU hybrid system with parallel edge sampling and negative sampling.

result GraphVite achieves high performance (50x faster) for large networks.

node embedding large networks CPU-GPU hybrid parallel sampling negative sampling

2019-03-02abs ↗pdf ↗

R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimizationq-fin.CP

RD-Agent(Q) automates quantitative finance research and development.

problem Challenges in asset return prediction due to high dimensionality and volatility.

method Data-centric multi-agent framework for automated research and development of quantitative strategies.

result Up to 2X higher annualized returns with 70% fewer factors.

quantitative finance multi-agent systems factor mining model innovation deep time-series models

2025-05-21abs ↗pdf ↗